Deep learning for survival analysis: a review

Abstract

The influx of deep learning (DL) techniques into the field of survival analysis in recent years has led to substantial methodological progress; for instance, learning from unstructured or high-dimensional data such as images, text or omics data. In this work, we conduct a comprehensive systematic review of DL-based methods for time-to-event analysis, characterizing them according to both survival- and DL-related attributes. In summary, the reviewed methods often address only a small subset of tasks relevant to time-to-event data—e.g., single-risk right-censored data—and neglect to incorporate more complex settings. Our findings are summarized in an editable, open-source, interactive table: https://survival-org.github.io/DL4Survival. As this research area is advancing rapidly, we encourage community contribution in order to keep this database up to date.

Development and Validation of Prognostic Models Using Radiomic Features from Pre-Treatment Positron Emission Tomography (PET) Images in Head and Neck Squamous Cell Carcinoma (HNSCC) Patients

“…However, recognizing the possibility of inter-operator variability, it is recommended to include multiple radiologists for tumor segmentation to ensure reliability and reproducibility of segmentation results, by following a clear protocol [ 53 ]. Finally, we explored only a selected set of machine learning models to identify the best-performing model, while several other machine learning or deep learning algorithms for time-to-event analysis have been recently reported [ 54 , 55 ].…”

Survival prediction landscape: an in-depth systematic literature review on activities, methods, tools, diseases, and databases

“…In Table 2 , a comprehensive analysis of the scope of review articles indicates that existing studies can be classified into three distinct groups. (I) Nine review papers primarily focus on the application of DL algorithms in survival prediction (Ahmed, 2005 ; Bakasa and Viriri, 2021 ; Kvamme and Borgan, 2021 ; Pobar et al, 2021 ; Kantidakis et al, 2022 ; Altuhaifa et al, 2023 ; Salerno and Li, 2023 ; Wekesa and Kimwele, 2023 ; Wiegrebe et al, 2023 ), (II) seven review papers summarize the application of ML algorithms in survival prediction (Gupta et al, 2018 ; Lee and Lim, 2019 ; Boshier et al, 2022 ; Guan et al, 2022 ; Mo et al, 2022 ; Wissel et al, 2022 ; Feldner-Busztin et al, 2023 ), andsix review papers summarize survival prediction methods from three different categories namely statistical, ML, and DL methods (Bashiri et al, 2017 ; Herrmann et al, 2021 ; Tewarie et al, 2021 ; Westerlund et al, 2021 ; Deepa and Gunavathi, 2022 ; Rahimi et al, 2023 ).…”

“…For instance, Feldner-Busztin et al ( 2023 ) despite their focus on dimensionality reduction, fall short in providing a comprehensive summary of current trends in feature engineering approaches with respect to diseases and data modalities. Furthermore, a small portion of these review papers cover details of few state of the art survival prediction models (Ahmed, 2005 ; Kantidakis et al, 2022 ; Wiegrebe et al, 2023 ). While current review papers summarize survival prediction pipelines partially, there is a necessity to bring diverse information into a unified platform which offers comprehensive insights into patterns and trends associated with survival prediction pipelines.…”

Review: Deep Learning-Based Survival Analysis of Omics and Clinicopathological Data

“…The old failure to outperform the Cox model was explained with the lack of infrastructure and the under-developed theoretical apparatus. A large number of primary research articles on the topic have been published, and an interested reader can be referred to a review by [4] for an exhaustive enumeration of the algorithms up to to the end of 2022 and their systematic characterization in the space of the alternatives of DL. Currently, the motivation for developing neural network-based survival algorithms is broader than that of outperforming the classical survival methods and includes developing a principally new research paradigm for fundamental biological research.…”